The objective of the project is to characterize the potential role of DNA methylation patterns in Sjgren's Syndrome (SS) disease etiology and mechanism. A growing body of evidence indicates that epigenetic changes, in particular, altered patterns of DNA methylation, contribute to the development of autoimmune disease and can mediate genetic risk factors. To date, aberrant DNA methylation has been associated with several human autoimmune diseases, including rheumatoid arthritis and systemic lupus erythematosus. My proposal focuses on the identification of unique epigenetic profiles in SS, focusing primarily on T cells, B cells, and salivary gland tissue, a primary site of the inflammatory disease. An understanding of these profiles in the context of genetic background, publicly available genomic data (RNA, ChIP-seq, WGBS) and annotation data (TF binding motifs, genomic QTLs), and cell types have the potential to significantly transform our understanding of SS etiology, particularly given the well-established heterogeneity in clinical manifestations. Further, these DNA methylation profiles could become useful clinical biomarkers for determining risk and prognosis in SS, especially if their biological relevance can be validated in future functional studies. The overall hypothesis of this project is that DNA methylation changes in key genomic regions are associated with SS status and phenotype. The project will use genome-wide SNP data, epigenome-wide methylation array data, and clinical characteristics abstracted from 100 SS cases and 20 controls to address 3 related hypotheses: First, that epigenome-wide DNA methylation patterns in T cells, B cells (both derived from peripheral blood), and salivary gland biopsy tissue are associated with primary SS status after correcting for cell-type heterogeneity. Case-control differences will be tested using supervised machine learning algorithms, new region-discovery methods, and cell-mixture inference techniques. Publicly available data (listed above) will be used to infer biological relevance. Second, that epigenome-wide DNA methylation patterns in these samples are representative of SS sub-phenotypes. Cases (and initially controls) will be clustered using principal component analysis (PCA) applied to all relevant DNA methylation probes and subsets of probes measuring CpGs implicated in specific pathways relevant to SS. The reduced representations will be tested for association with specific clinical and serological profiles. Third, that risk alleles identifie by genome-wide association studies (GWAS) are directly or indirectly associated with SS-specific DNA methylation patterns. Allelic dosage and random forest selection frequency (RFSF) analyses will be used to identify novel methylation quantitative trait loci within SS-associated genes in cases and controls (separately), and we will test whether cell-proportion or eQTLs can explain SS-specific methylation patterns.